Clustered regularly interspaced short palindromic repeats (CRISPR) is a promising novel technology that holds the potential of treating genetic diseases. Safety and specificity of the treatment are to be further studied and developed prior to implementation of the technology into the clinic. The guide-RNA (gRNA) allows precise position-specific DNA targeting, although it may tolerate small changes such as point mutations. The permissive nature of the CRISPR-Cas system makes allele-specific targeting a challenging goal. Hence, an allele-specific targeting approach is in need for future treatments of heterozygous patients suffering from diseases caused by dominant negative mutations. The single-nucleotide polymorphism (SNP)-derived protospacer adjacent motif (PAM) approach allows highly allele-specific DNA cleavage due to the existence of a novel PAM sequence only at the target allele. Here, we present CrisPam, a computational tool that detects PAMs within the variant allele for allele-specific targeting by CRISPR-Cas systems. The algorithm scans the sequences and attempts to identify the generation of multiple PAMs for a given reference sequence and its variations. A successful result is such that at least a single PAM is generated by the variation nucleotide. Since the PAM is present within the variant allele only, the Cas enzyme will bind the variant allele exclusively. Analyzing a dataset of human pathogenic point mutations revealed that 90% of the analyzed mutations generated at least a single PAM. Thus, the SNP-derived PAM approach is ideal for targeting most of the point mutations in an allele-specific manner. CrisPam simplifies the gRNAs design process to specifically target the allele of interest and scans a wide range of 26 unique PAMs derived from 23 Cas enzymes.

Base editing is a genome-editing approach that employs the CRISPR/Cas system to precisely install point mutations within the genome. A deaminase enzyme is fused to a deactivated Cas and enables transition conversions. The diversified repertoire of base editors provides a wide range of base editing possibilities. However, existing base editors cannot induce transversion substitutions and activate only within a specified region relative to the binding site, thus, they cannot precisely correct every point mutation. Here, we present BE-FF (Base Editors Functional Finder), a novel computational tool that identifies suitable base editors to correct the translated sequence erred by a point mutation. When a precise correction is impossible, BE-FF aims to mutate bystander nucleotides in order to induce synonymous corrections that will correct the coding sequence. To measure BE-FF practicality, we analysed a database of human pathogenic point mutations. Out of the transition mutations, 60.9% coding sequences could be corrected. Notably, 19.4% of the feasible corrections were not achieved by precise corrections but only by synonymous corrections. Moreover, 298 cases of transversion-derived pathogenic mutations were detected to be potentially repairable by base editing via synonymous corrections, although base editing is considered impractical for such mutations.

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ABSTRACTBase editing is a genome-editing approach that employs the CRISPR/Cas system to precisely install point mutations within the genome. A cytidine or adenosine deaminase enzyme is fused to a deactivated Cas and converts C to T or A to G, respectively. The diversified repertoire of base editors, varied in their Cas and deaminase proteins, provides a wide range of functionality. However, existing base-editors can only induce transition substitutions in a specified region determined by the base editor, thus, they are incompatible for many point mutations. Here, we present BE-FF (Base Editors Functional Finder), a novel computational tool that identifies suitable base editors to correct the translated sequence erred by a given single nucleotide variation. Even if a perfect correction of the single nucleotide variation is not possible, BE-FF detects synonymous corrections to produce the reference protein. To assess the potential of BE-FF, we analysed a database of human pathogenic point mutations and found suitable base editors for 60.9% of the transition mutations. Importantly, 19.4% of them were made possible only by synonymous corrections. Moreover, we detected 298 cases in which pathogenic mutations caused by transversions were potentially repairable by base editing via synonymous corrections, although it had been thought impractical. The BE-FF tool and the database are available at https://www.danioffenlab.com/be-ff.GRAPHICAL ABSTRACT